Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents

Fikret Akdeniz, Metin Biçil, Yusuf Karadede, Füreya Elif Özbek and Gültekin Özdemir

Energy, 2018, vol. 160, issue C, 1047-1054

Abstract: The higher heating values (HHVs) of 11 non-wood lignocellulosic materials from Turkey were measured experimentally and calculated incorporating various theoretical models with the values of both lignin and extractive contents. Multiple linear regression (MLR) and real valued genetic algorithm (RVGA) were used to derive the theoretical models. A non-linear RVGA6 model was determined as the best non-linear model considering the experimental results with a regression coefficient of 92% coefficient of determination (R2), 0.301 sum of squared errors (SSE), 0.301 mean squared errors (MSE), 0.548 root mean squared errors (RMSE) and 0.0187 mean absolute percentage error (MAPE) and is proposed as a better alternative for theoretical HHV calculations to the multiple linear modellings such as MLR and RVGA1.

Keywords: Lignocellulosic materials; Higher heating value; Data fitting; Regression; Genetic algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218313501
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:160:y:2018:i:c:p:1047-1054

DOI: 10.1016/j.energy.2018.07.053

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().